Given that Musk has absolutely destroyed the Twitter API for academic use, I had to instead rely on what happens on Bluesky. Bluesky is well known for having a clear left-wing tilt, while also being the posting retirement home for elder millennials. So this is not exactly a representative sample of what goes viral in polling, but it is in line with what I found a long time ago looking at Twitter. In brief, I scraped the last 90 days of posts from @usapolling.bsky.social, who appears to be the most active “polling distribution” account on Bluesky. After that, I used an LLM and an overly complicated prompt (see below) to extract the information in each post. I then calculated the margin for the race(s) in each post and finally calculated the average margin for each race (Dem-Rep). To further simplify things, this analysis focuses only on posts that included a single general election poll. Nothing is perfect, but I probably should be spending time working on publishable research and lectures anyway.

Figure 1 shows the number of reposts for each poll post by the difference from the average margin for that race. Moving to the right along the x-axis means that a poll was more favorable for Democrats than other polls in the race. I’ve filtered this to include only races where there were multiple polls posted. The black line shows a line of best fit, indicating that as polls become “better” for Democrats (further from the average poll in that race), they tend to be reposted more.

Code

library(tidyverse)
library(lme4)
library(broom.mixed)
library(huxtable)
library(ggeffects)

df <- read_csv(here::here("blog/data/election_polling.csv"))

df <- df |>
  mutate(Date = as.Date(time)) |>
  mutate(
    ID = as.factor(ID),
    Seat = as.factor(Seat),
    Date_Numeric = as.numeric(Date) - min(as.numeric(Date))
  ) |>
  mutate(
    ID = case_when(
      ID == "Generic" & Seat == "GenericCongress" ~ "Generic-Congress",
      .default = paste0(ID, "-", Seat)
    )
  ) |>
  filter(!(ID == "TX" & Seat == "GenericCongress"))

df |>
  filter(N > 1) |>
  ggplot(aes(x = Margin_Diff, y = reposts, color = Date)) +
  geom_point() +
  scale_y_log10() +
  geom_smooth(method = "lm", color = "black") +
  theme_minimal() +
  theme(legend.position = "bottom") +
  scale_color_viridis_c(trans = "date") +
  labs(
    x = "Difference from Average Margin",
    y = "Number of Reposts\n(Logged)"
  )

Figure 1: Reposts by Difference from Average Margin

It is also possible to look at race-level variation. In Figure 2, I show the same style of plot broken out by race (and one for just generic congressional polls). I’ve only included races where there were at least 10 polls (or else it is just overwhelming). We see the same pattern for almost every one of those races: the most viral posts are the ones that favor Democrats more than the average post (with the one exception being the NC Senate race for some reason).

Code

df |>
  group_by(
    ID
  ) |>
  filter(N > 10) |>
  ggplot(aes(x = Margin_Diff, y = reposts, color = Date)) +
  geom_point() +
  scale_y_log10() +
  geom_smooth(method = "lm", color = "black") +
  facet_wrap(~ID) +
  scale_color_viridis_c(trans = "date") +
  labs(
    x = "Difference from Average Margin",
    y = "Number of Reposts\n(Logged)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")

Figure 2: Race by Race - Reposts by Difference from Average Margin

We can go a step further and estimate a model predicting the number of reposts based on the Dem-Rep margin, the post margin minus the average margin, and the date the post was shared. To account for the fact that we are mixing a bunch of races together, I estimate a model with random intercepts for each race (I exclude the generic congressional race and include only races with more than 5 posts). The predicted number of reposts across values of the difference from the average margin and the margin itself is displayed in Figure 3. Again, this shows that the distance from the average margin is what dictates post virality, with the margin itself being relatively unrelated.

Code

mod <- lmer(
  log(reposts) ~ Margin_Diff + Margin + Date_Numeric + (1 | ID),
  data = df,
  subset = reposts > 0 & N > 5 & N < 50
)

ggpredict(mod, terms = c("Margin_Diff [-10:10] ")) |>
  plot(show_title = FALSE) +
  scale_y_continuous(limits = c(0, 20)) +
  labs(
    x = "Difference from Average Margin",
    y = "Number of Reposts"
  )
ggpredict(mod, terms = c("Margin[-20:20]")) |>
  plot(show_title = FALSE) +
  scale_y_continuous(limits = c(0, 20)) +
  labs(
    x = "Dem-Rep Margin",
    y = "Number of Reposts"
  )

#huxreg(mod)

(a) Predicted Reposts Based on Diff from Avg Margin

(b) Predicted Reposts based on Margin

Figure 3: Predictions from Random Intercept Model

If we include random slopes as well, this can tell us something interesting about which races are most likely to go viral right now. In Figure 4, I show the random effects of a model with random slopes and random intercepts. This can be read as indicating how different each race is from other races. For example, the TX-Senate race is likely to get more reshares in general, and a post that shows the Democrat up is going to go especially well. In contrast, no one cares about the Pennsylvania governor’s race (which might have to do with Bluesky’s feelings towards Josh Shapiro).

Code

mod <- lmer(
  log(reposts) ~ Margin_Diff + Margin + Date_Numeric + (1 + Margin_Diff | ID),
  data = df,
  subset = reposts > 0 & N > 5 & N < 50
)

sjPlot::get_model_data(mod, type = "re", sort.est = "Margin_Diff") |>
  mutate(facet = ifelse(facet == "ID (Intercept)", "Intercept", "Slope")) |>
  ggplot(aes(
    x = estimate,
    xmin = conf.low,
    xmax = conf.high,
    y = reorder(term, reihe)
  )) +
  geom_pointrange() +
  facet_wrap(~facet, scales = "free_x") +
  theme_minimal() +
  labs(y = "Estimate", x = "")

Figure 4: Random Effects Estimates

NoteAI Use

The Python code (below) was written using OpenRouter’s Auto Router, which routes LLM requests to models. It routes requests to different models based on a classification model run by OpenRouter, aiming to minimize cost while maximizing quality. I have it set to use only Zero Data Retention models (for what that is worth) and to the “High” cost tier. The R code and prose were written by hand, with one run of an LLM to identify typos and grammar issues in the prose.

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The original post continues with the full Python data-extraction code and LLM prompt.

Originally published at kevinreuning.com.